Computer aided breast calcification auto-detection in cone beam breast CT
Computer aided breast calcification auto-detection in cone beam breast CT
复制标题
锥形束乳腺CT计算机辅助乳腺钙化自动检测
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Jiangkun Liu
中科院分区：
文献类型：
--
作者：
Xiaohua Zhang;R. Ning;Jiangkun Liu
在锥束乳腺CT(CBBCT)中，乳房钙化的信号高于周围组织。在没有乳房结构叠加的情况下，可以显示钙化的三维分布。在本研究中，基于钙化具有较高对比度的事实，使用局部阈值和直方图阈值来选择候选钙化区域。从每个候选钙化点中提取6个特征：平均前景CT数值、前景CT数值标准差、平均背景CT数值、背景CT数值标准差、前景背景对比度和平均边缘梯度。为了减少假阳性候选钙化，设计了前馈反向传播人工神经网络。将人工神经网络与确认钙化的放射科医生一起训练，并在钙化自动检测任务中用作分类器。在初步实验中，测试数据集中90%的钙化被正确检测，每个数据集平均有10个假阳性。
In Cone Beam Breast CT (CBBCT), breast calcifications have higher intensities than the surrounding tissues. Without the superposition of breast structures, the three-dimensional distribution of the calcifications can be revealed. In this research, based on the fact that calcifications have higher contrast, a local thresholding and a histogram thresholding were used to select candidate calcification areas. Six features were extracted from each candidate calcification: average foreground CT number value, foreground CT number standard deviation, average background CT number value, background CT number standard deviation, foreground-background contrast, and average edge gradient. To reduce the false positive candidate calcifications, a feed-forward back propagation artificial neural network was designed. The artificial neural network was trained with the radiologists confirmed calcifications and used as classifier in the calcification auto-detection task. In the preliminary experiments, 90% of the calcifications in the testing data sets were detected correctly with an average of 10 false positives per data set.
一种改进的计算机辅助诊断方案，使用小波变换检测数字乳房 X 光照片中的簇状微钙化。
影响因子：
4.8
作者：
Yoshida,H;Doi,K;Nishikawa,RM;Giger,ML;Schmidt,RA
通讯作者：
Schmidt,RA
影响因子：
19.7
作者：
WU, YZ;GIGER, ML;METZ, CE
通讯作者：
METZ, CE